SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 561–570 of 982 papers

TitleStatusHype
Embedding Graphs on Grassmann ManifoldCode0
Dynamic Graph Learning Based on Hierarchical Memory for Origin-Destination Demand PredictionCode1
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements—0
Recipe for a General, Powerful, Scalable Graph TransformerCode2
KQGC: Knowledge Graph Embedding with Smoothing Effects of Graph Convolutions for Recommendation—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Relphormer: Relational Graph Transformer for Knowledge Graph RepresentationsCode1
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified